- Deep expertise in either AI quality and applied modeling or database systems, supported by substantial technical contributions and measurable production impact. Expertise in both is not required.
- 8+ years of relevant experience in software engineering, applied machine learning, or database systems, including independently leading multi-engineer projects from definition through production.
- Strong fundamentals in algorithms, data structures, and software design; fluency in at least one of C++, Java, or Python; and strong debugging and testing skills.
- An empirical approach to problem solving: clear metrics, disciplined experiments, root-cause analysis, and sound tradeoffs between quality, cost, and latency.
- Strong product sense and a track record of co-defining product direction with PMs and leadership, connecting technical investments to customer value and business outcomes.
Relevant depth in either area
AI quality: modeling, evaluation methodology, statistical experimentation, data quality, error analysis, and automated optimization. Prior hands-on experience with model fine-tuning and post-training is a plus for AI quality candidates, but is not required.
Database systems: query optimizers, compilers, execution engines, distributed data processing, and performance analysis. Familiarity with AI infrastructure is a plus; our systems focus is integrating AI into database execution and data pipelines.
Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.
How do you want to make your impact?
For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com http://careers.snowflake.com